User query history expansion for improving language model adaptation

ABSTRACT

Query history expansion may be provided. Upon receiving a spoken query from a user, an adapted language model may be applied to convert the spoken query to text. The adapted language model may comprise a plurality of queries interpolated from the user&#39;s previous queries and queries associated with other users. The spoken query may be executed and the results of the spoken query may be provided to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patent application Ser. No. 13/242,912, entitled “USER QUERY HISTORY EXPANSION FOR IMPROVING LANGUAGE MODEL ADAPTATION”, filed on Sep. 23, 2011, the entire disclosure of which is hereby incorporated herein by reference.

BACKGROUND

Adaptation of a language model may be based on a user's historic inputs to a speech recognition algorithm. Conventional language model adaptation has been used in in desktop dictation and other applications that can accumulate a significant amount of user historic voice input and related text, with or without supervision. However, this approach allows for only limited accuracy gain in tasks where large amounts of prior history are unavailable, such as voice search tasks.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this Summary intended to be used to limit the claimed subject matter's scope.

Query history expansion may be provided. Upon receiving a spoken query from a user, an adapted language model may be applied to convert the spoken query to text. The adapted language model may comprise a plurality of queries interpolated from the user's previous queries and queries associated with other users. The spoken query may be executed and the results of the spoken query may be provided to the user.

Both the foregoing general description and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing general description and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present invention. In the drawings:

FIG. 1 is a block diagram of an operating environment;

FIG. 2 is a flow chart of a method for providing query history expansion; and

FIG. 3 is a block diagram of a computing device.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While embodiments of the invention may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the invention.

For a voice search application, a user's historic search queries, including both voice and text requests, may be used to create an adaptation data set that includes not only the historic search queries themselves but also other queries considered relevant for the original historic queries. The augmentation of the original query set may be performed, for example, by leveraging the user-query association data to either incorporate historic queries from other users who share some commonality with the target user and/or to identify a cluster of users that the target user is similar to and adopt the pooled adaptation model from those users in the cluster. For a second example, the query-click association data may be leveraged to identify all other queries that share the same click URLs with a user's historic queries and/or other queries that are within a certain distance from the historic queries in a query-click graph. A query-click graph may comprise a bipartite graph with queries on one side and clicked documents on the other. A user's query may be associated with a document, which may be represented by a uniform resource locator (URL), if the querying user selects that link. Yet another example may comprise selecting queries from a large set of general search queries that are similar to the user's historic queries in terms of lexical match, word and phrase class match, and/or match in some latent attributes such as query topics.

FIG. 1 is a block diagram of an operating environment 100 for providing query history expansion comprising a spoken dialog system (SDS) 110. SDS 110 may comprise a plurality of reference results 115 and a spoken language understanding application 120. SDS 110 may be operative to interact, directly and/or over a network 130, with a user device 135 and/or a network application 140, such as a search engine. Network 130 may comprise a private network, such as a corporate local area network, a cellular network, and/or a public network such as the Internet. User device 135 may comprise an electronic communications device such as a computer, laptop, cellular and/or IP phone, tablet, game console and/or other device. User device 135 may be coupled to a capture device 150 that may be operative to record a user and capture spoken words, motions and/or gestures made by the user, such as with a camera and/or microphone. User device 135 may be further operative to capture other inputs from the user such as by a keyboard, touchscreen and/or mouse (not pictured). Consistent with embodiments of the invention, capture device 150 may comprise any speech and/or motion detection device capable of detecting the speech and/or actions of the user. For example, capture device 150 may comprise a Microsoft® Kinect® motion capture device comprising a plurality of cameras and a plurality of microphones.

FIG. 2 is a flow chart setting forth the general stages involved in a method 200 consistent with an embodiment of the invention for providing query history expansion. Method 200 may be implemented using a computing device 300 as described in more detail below with respect to FIG. 3. Ways to implement the stages of method 200 will be described in greater detail below. Method 200 may begin at starting block 205 and proceed to stage 210 where computing device 300 may collect a plurality of previous search queries associated with a user. For example, the plurality of previous search queries may have been received as gestures, spoken queries, typed queries, and/or queries selected via an input device.

Method 200 may then advance to stage 215 where computing device 300 may create an adaptation data set associated with the user according to the plurality of previous queries. For example, SLU module 120 may use the previous queries in a language model as training data to aid in converting new queries received from the user to text. Upon receiving a query such as “weather in Seattle,” the language model may enable SLU module 120 to convert the query to text with greater accuracy and/or confidence by recognizing that the same query has been received before.

Method 200 may then advance to stage 220 where computing device 300 may expand the adaptation data set by adding additional queries from other users. For example, queries may be added from users who share at least one common characteristic with the user, where each of the new queries comprises a lexical similarity to at least one of the user's previous queries, and/or where each of the new queries is associated with the same web link result as one of the user's previous search queries.

Method 200 may then advance to stage 225 where computing device 300 may interpolate the user's previous queries and the queries from the other users into the adaptation data set for a personalized language model. For example, duplicate entries may be recognized and weighted more heavily as being more likely to occur.

Method 200 may then advance to stage 230 where computing device 300 may process queries from the user according to the adapted data set. For example, upon receiving a new search query spoken by the user, SLU 120 may convert the search query to text according to the personalized language model, execute the converted query, and provide at least one result of the executed query to the user. Method 200 may then end at stage 240.

An embodiment consistent with the invention may comprise a system for providing query history expansion. The system may comprise a memory storage and a processing unit coupled to the memory storage. The processing unit may be operative to receive a spoken query from a user apply an adapted language model to convert the spoken query to text, execute the spoken query, and provide at least one result of the spoken query to the user.

Another embodiment consistent with the invention may comprise a system for providing query history expansion. The system may comprise a memory storage and a processing unit coupled to the memory storage. The processing unit may be operative to receive a plurality of previous queries associated with a user, retrieve a second plurality of previous queries associated with a plurality of other users, interpolate at least a subset of the second plurality of previous queries with the plurality of previous queries associated with the user, create an adapted language model according to the interpolated queries, receive a new query from the user, and convert the new query into a text string according to the adapted language model. The converted query may then be provided to a spoken language understanding (SLU) application that may, for example, process the query and return a plurality of search results to the user. The processing unit may be further operative to annotate each of the second plurality of queries and each of the plurality of previous queries associated with the user and select the at least a subset of the second plurality of previous queries for interpolation according to a common annotation between at least one of the second plurality of previous queries and at least one of the plurality of queries associated with the user. For example, the user may be associated with a previous query of “weather in San Francisco.” Such a query may be annotated, such as with XML tags, as “weather in <city>San Francisco</city>.” A query among the other users' queries may comprise “weather in Seattle,” which may be annotated as “weather in <city>Seattle</city>.” This may permit the system to match the two queries by their similar tags, and may enable the system to expand the interpolated query set according to latent attributes such as the topics of the queries even where the literal subjects (e.g., Seattle and San Francisco) are not lexically similar.

Yet another embodiment consistent with the invention may comprise a system for providing query history expansion. The system may comprise a memory storage and a processing unit coupled to the memory storage. The processing unit may be operative to collect a plurality of previous search queries associated with a user and expand the adaptation data set by adding a plurality of historical queries from a plurality of users, wherein the plurality of users share at least one common characteristic with the user, adding a second plurality of queries, wherein each of the second the plurality of queries comprises a lexical similarity to at least one of the previous search queries associated with the user, and adding a third plurality of queries, wherein each of the third plurality of historical queries is associated with a web link result, wherein each web link result is further associated with at least one of the plurality of previous search queries. The processing unit may be further operative to interpolate each of the plurality of previous search queries, the plurality of historical queries, the second plurality of queries, and the third plurality of queries into an adaptation data set for a personalized language model and, upon receiving a new search query spoken by the user, convert the search query to text according to the personalized language model, execute the converted query, and provide at least one result of the executed query to the user.

The embodiments and functionalities described herein may operate via a multitude of computing systems, including wired and wireless computing systems, mobile computing systems (e.g., mobile telephones, tablet or slate type computers, laptop computers, etc.). In addition, the embodiments and functionalities described herein may operate over distributed systems, where application functionality, memory, data storage and retrieval and various processing functions may be operated remotely from each other over a distributed computing network, such as the Internet or an intranet. User interfaces and information of various types may be displayed via on-board computing device displays or via remote display units associated with one or more computing devices. For example user interfaces and information of various types may be displayed and interacted with on a wall surface onto which user interfaces and information of various types are projected. Interaction with the multitude of computing systems with which embodiments of the invention may be practiced include, keystroke entry, touch screen entry, voice or other audio entry, gesture entry where an associated computing device is equipped with detection (e.g., camera) functionality for capturing and interpreting user gestures for controlling the functionality of the computing device, and the like. FIG. 3 and the associated descriptions provide a discussion of a variety of operating environments in which embodiments of the invention may be practiced. However, the devices and systems illustrated and discussed with respect to FIG. 3 are for purposes of example and illustration and are not limiting of a vast number of computing device configurations that may be utilized for practicing embodiments of the invention, described herein.

FIG. 3 is a block diagram of a system including computing device 300. Consistent with an embodiment of the invention, the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device 300 of FIG. 3. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit. For example, the memory storage and processing unit may be implemented with computing device 300 or any of other computing devices 318, in combination with computing device 300. The aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned memory storage and processing unit, consistent with embodiments of the invention. Furthermore, computing device 300 may comprise operating environment 300 as described above. Methods described in this specification may operate in other environments and are not limited to computing device 300.

With reference to FIG. 3, a system consistent with an embodiment of the invention may include a computing device, such as computing device 300. In a basic configuration, computing device 300 may include at least one processing unit 302 and a system memory 304. Depending on the configuration and type of computing device, system memory 304 may comprise, but is not limited to, volatile (e.g. random access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 304 may include operating system 305, one or more programming modules 306, and may include Spoken Language Understanding module 120. Operating system 305, for example, may be suitable for controlling computing device 300's operation. Furthermore, embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 3 by those components within a dashed line 308.

Computing device 300 may have additional features or functionality. For example, computing device 300 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 3 by a removable storage 309 and a non-removable storage 310. Computing device 300 may also contain a communication connection 316 that may allow device 300 to communicate with other computing devices 318, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 316 is one example of communication media.

The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory 304, removable storage 309, and non-removable storage 310 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 300. Any such computer storage media may be part of device 300. Computing device 300 may also have input device(s) 312 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. Output device(s) 314 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

The term computer readable media as used herein may also include communication media. Communication media may be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

As stated above, a number of program modules and data files may be stored in system memory 304, including operating system 305. While executing on processing unit 302, programming modules 306 (e.g., Spoken Language Understanding module 120) may perform processes and/or methods as described above. The aforementioned process is an example, and processing unit 302 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.

Generally, consistent with embodiments of the invention, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Furthermore, embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.

Embodiments of the invention, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present invention may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

Embodiments of the invention may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated in FIG. 3 may be integrated onto a single integrated circuit. Such an SOC device may include one or more processing units, graphics units, communications units, system virtualization units and various application functionalities, all of which may be integrated (or “burned”) onto the chip substrate as a single integrated circuit. When operating via an SOC, the functionality, described herein, with respect to training and/or interacting with SDS 110 may operate via application-specific logic integrated with other components of the computing device/system X on the single integrated circuit (chip).

Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

While certain embodiments of the invention have been described, other embodiments may exist. Furthermore, although embodiments of the present invention have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the invention.

All rights including copyrights in the code included herein are vested in and the property of the Applicants. The Applicants retain and reserve all rights in the code included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

While certain embodiments of the invention have been described, other embodiments may exist. While the specification includes examples, the invention's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as example for embodiments of the invention. 

We claim:
 1. A computer-implemented method comprising: receiving a spoken query from a user; applying an adapted language model to process the spoken query, wherein the adapted language model processes data associated with the spoken query using a set of previous queries of the and previous query data of a plurality of other users that share at least one common characteristic with the set of previous queries of the user, wherein the common characteristic is selected uniform resource locator data included in the set of previous queries of the user and included in the previous query data of the plurality of other users; and modifying the data associated with the spoken query based on the applying of the adapted language model.
 2. The computer-implemented method of claim 1, wherein the receiving of the spoken query further comprises converting the spoken query to text, and wherein the modifying of the data associated with the spoken query further comprises modifying the converted text.
 3. The computer-implemented method of claim 1, wherein the modifying of the data associated with the spoken query further comprises converting the spoken query to text for subsequent processing.
 4. The computer-implemented method of claim 1, further comprising generating a personalized language model for the user based on the adapted language model, and applying the personalized language model to a subsequent query of the user.
 5. The computer-implemented method of claim 1, wherein the applying of the adapted language model further comprises analyzing a click-query graph to identify the common characteristic between at least one previous query of the user and at least a previous query of another user, and using result data from the analyzing of the click-query graph to evaluate the spoken query.
 6. The computer-implemented method of claim 5, wherein the query-click graph is a bipartite graph providing the previous query data in association with content of the selected uniform resource locator data.
 7. The computer-implemented method of claim 1, wherein the applying of the adapted language model further comprises identifying a cluster of users that are associated with the previous query data, and using additional query data of the cluster of users to expand the adapted language model in the converting of the spoken query.
 8. The computer-implemented method of claim 1, wherein the applying identifies, from the previous query data, queries that include the selected uniform resource locator that corresponds with content having a lexical similarity to the set of previous queries of the user, and the generating of the result data utilizes the identified queries to process the spoken query, and wherein the lexical similarity is at least one selected from a group consisting of: a lexical match between the set of previous queries and the previous query data, a word class match between the set of previous queries and the previous query data, a phrase class match between the set of previous queries and the previous query data, a match in latent attributes between the set of previous queries and the previous query data, and a match in a query topic between the set of previous queries and the previous query data.
 9. A system comprising: at least one processor; and a memory operatively connected with the processor storing instructions, that when executed on the processor causing the processor to execute operations that comprise: creating an adapted language model to process queries received from a user, wherein the adapted language model expands a set of previous queries of the user by interpolating previous query data of a plurality of other users that share at least one common characteristic with the user, wherein the common characteristic is selected uniform resource locator data included in the set of previous queries of the user and included in the previous query data of the plurality of other users, and processing data associated with the spoken query by applying the created adapted language model to the data associated with the spoken query.
 10. The system of claim 9, wherein the processing of the data associated with the spoken query further comprises converting the spoken query to text for subsequent processing.
 11. The system of claim 9, wherein the operations further comprising generating a personalized language model for the user based on the adapted language model, and applying the personalized language model to a subsequent query of the user.
 12. The system of claim 9, wherein the processing comprises analyzing a click-query graph to identify the common characteristic between at least one previous query of the user and at least a previous query of another user, and using result data from the analyzing of the click-query graph to convert the spoken query.
 13. The system of claim 12, wherein the query-click graph is a bipartite graph providing the previous query data in association with the content of the selected uniform resource locator data.
 14. The system of claim 9, wherein the applying of the adapted language model further comprises identifying a cluster of users that are associated with the previous query data, and using additional query data of the cluster of users to expand the adapted language model in processing the data of the spoken query.
 15. The system of claim 9, wherein the applying identifies, from the previous query data, a subset of queries that include selected uniform resource locator data that corresponds with the content having a lexical similarity to the set of previous queries of the user, and the generating of the result data utilizes the identified subset of queries to generate the result data, and wherein the lexical similarity is at least one selected from a group consisting of: a lexical match between the set of previous queries and the previous query data, a word class match between the set of previous queries and the previous query data, a phrase class match between the set of previous queries and the previous query data, a match in latent attributes between the set of previous queries and the previous query data, and a match in a query topic between the set of previous queries and the previous query data.
 16. A computer-readable storage medium which stores a set of instructions which when executed on at least one processor, the processor performs a method comprising: receiving a spoken query from a user; and applying an adapted language model to convert the spoken query to text, wherein the adapted language model expands a set of previous queries of the user to be applied to covert the spoken query by incorporating previous query data of a plurality of other users that share at least one common characteristic with the set of previous queries of the user, wherein the common characteristic is selected uniform resource locator data included in the set of previous queries of the user and included in the previous query data of the plurality of other users.
 17. The computer-readable storage medium of claim 16, wherein the method executed by the processor further comprises analyzing a click-query graph to identify the common characteristic between at least one previous query of the user and at least a previous query of another user, and converting the spoken query using result data from the analyzing of the click-query graph.
 18. The computer-readable storage medium of claim 17, wherein the query-click graph is a bipartite graph providing the previous query data in association with content of the clicked uniform resource locator.
 19. The computer-readable storage medium of claim 16, wherein the method executed by the processor further comprises generating a personalized language model for the user based on the adapted language model, and applying the personalized language model to a subsequent query of the user.
 20. The computer-readable storage medium of claim 16, wherein the applying of the adapted language model further comprises identifying a cluster of users that are associated with the previous query data, and using additional query data of the cluster of users to convert the spoken query. 